As we welcome students, staff and visitors back to campus, we require those attending Melbourne Business School in person to be fully vaccinated. Read our COVID-19 Vaccination Requirements Policy to learn more.

View policy

Tomohiro Ando

Professor of Management

Tomohiro Ando is a Professor of Management (Business Administration).

He is the Director of Centre of Excellence: Big Data, AI and Analytics, and Co-organiser of the online seminar series "Frontiers of Big Data, AI and Analytics". Centre's mission is to solve business and society issues associated with Big Data, AI and Analytics by helping leaders in business and society.

He is also an academic convenor of the Melbourne Centre of Data Science, which is built out of a joint collaboration between Statistics and Computer Science at University of Melbourne. The centre's goal is to promote and engage in fundamental and interdisciplinary research, teaching and leadership in Data Science.

He taught at University of California Berkeley, University of California Los Angeles, and Keio University. At Melbourne Business School, he teaches Analytics for Strategic Management, Consulting and Data Analysis in Senior Executive MBA, Executive MBA, part-time MBA and Master of Business Analytics programs. He also teaches at Advanced Management Program, designed for senior executives.

Recent Publications

A spatial panel quantile model with unobserved heterogeneity’, Ando, T. Li, K. & Lu, L. 2022, Journal of Econometrics.

Quantile Connectedness: Modelling Tail Behaviour in the Topology of Financial Networks’, Ando, T. Greenwood-Nimmo. M. & Shin, Y. 2022, Management Science.

Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity’, Ando, T. Bai, J. & Li, K. 2022, Journal of Econometrics.

Quantile co-movement in financial markets: A panel quantile model with unobserved heterogeneity’, Ando, T. & Bai, J., 2020, Journal of the American Statistical Association.

A weight-relaxed model averaging approach for high-dimensional generalized linear models’, Ando, T. & Li, K.-C., 2017, Annals of Statistics.